機械学習:Machine Learning

Protected: Supervised learning and regularization

Overview of supervised learning regression, discriminant and regularization ridge function, L1 regularization, bridge regularization, elastic net regularization, SCAD, group regularization, generalized concatenated regularization, trace norm regularization as the basis of machine learning optimization methods used for digital transformation, artificial intelligence and machine learning tasks

2022.11.08

アルゴリズム:Algorithms微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Spatial statistics of Gaussian processes, with application to Bayesian optimization

Spatial statistics of Gaussian processes as an application of stochastic generative models used in digital transformation, artificial intelligence, and machine learning tasks, and tools ARD, Matern kernelsfor Bayesian optimization GPyOpt and GPFlow and GPyTorch

2022.11.07

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Implementation of model-free reinforcement learning in python (1) epsilon-greedy method

Implementation in python of the epsilon-Greedy method, a model-free reinforcement learning method for use in digital transformation, artificial intelligence, and machine learning tasks, multi-armed bandit

2022.11.03

アルゴリズム:Algorithmsマルチエージェントシステム幾何学:Geometry強化学習最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Hierarchical Temporal Memory and Clojure

Deep learning with hierarchical temporal memory and sparse distributed representation with Clojure for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks

2022.11.02

Clojurepythonアルゴリズム:Algorithmsグラフ理論スパースモデリングスパースモデリング微分積分:Calculus時系列データ解析最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Network analysis using Clojure (1) Width-first/depth-first search, shortest path search, minimum spanning tree, subgraphs and connected components

Network analysis using Clojure/loop for digital transformation , artificial intelligence and machine learning tasks, width-first/depth-first search, shortest path search, minimum spanning tree, subgraph and connected components

2022.10.31

Clojureアルゴリズム:Algorithmsグラフ理論セマンテックウェブ技術:Semantic web Technologyマルチエージェントシステム微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Integration of logic and rules with probability/machine learning

Integration of logic and rules with machine learning (inductive logic programming, statistical relational learning, knowledge-based model building, Bayesian nets, probabilistic logic learning, hidden Markov models) used for digital transformation, artificial intelligence, and machine learning tasks.

2022.10.30

Symbolic Logicアルゴリズム:Algorithmsエキスパートシステム:expertsystemグラフ理論セマンテックウェブ技術:Semantic web Technologyベイズ推定微分積分:Calculus推論技術:inference Technology数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics関係データ学習

Fundamentals of Continuous Optimization – Calculus and Linear Algebra

Fundamentals of Continuous Optimization - Calculus and Linear Algebra (Taylor's theorem, Hesse matrix, Landau's symbol, Lipschitz continuity, Lipschitz constant, implicit function theorem, Jacobi matrix, diagonal matrix, eigenvalues, nonnegative definite matrix, positive definite matrix, subspace, projection, 1-rank update, natural gradient method, quasi Newton method, Sherman-Morrison formula, norm, Euclidean norm, p-norm, Schwartz inequality, Helder inequality, function on matrix space)

2022.10.28

アルゴリズム:Algorithmsスパースモデリング微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Calculation of marginal likelihood, posterior mean, posterior covariance, and predictive distribution using variational Bayesian methods

Methods for computing marginal likelihoods, posterior means, posterior covariances, and predictive distributions in variational Bayesian methods for digital transformation, artificial intelligence , and machine learning tasks James Stein estimator, maximum likelihood estimation, empirical Bayes estimator, Bayesian free energy, hyperparameters, automatic relevance determination, linear regression models, stochastic complexity, log marginal likelihood empirical Bayesian learning, multinomial distribution models, posterior means, linear regression models

2022.10.27

アルゴリズム:Algorithmsベイズ推定微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Structural learning of graphical models

On learning graph structures from data in Bayesian networks and Markov probability fields (Max-Min Hill Climbing (MMHC), Chow-Liu's algorithm, maximizing the score function, PC (Peter Spirtes and Clark Clymoir) Algorithm, GS (Grow-Shrink) algorithm, SGS (Spietes Glymour and Scheines) algorithm, sparse regularization, independence condition)

2022.10.26

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus推論技術:inference Technology最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Nonparametric Bayes from the viewpoint of point processes – Normalized gamma processes, Dirichlet processes and complete random measures

Nonparametric Bayes from the viewpoint of point processes utilized in digital transformation, artificial intelligence and machine learning tasks - Normalized gamma and Dirichlet processes and complete random measures Poisson processes, Livy measures, gamma random measures, beta random measures, Levy-Ito decomposition

2022.10.25

アルゴリズム:Algorithmsベイズ推定微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory